摘要：Time-frequency (TF) analysis is a useful tool for seismic data processing and interpretation. We introduce sparse Bayesian learning (SBL) to TF analysis and propose a new SBL-based high-resolution TF method. The method decomposes the seismic trace into a series of Ricker wavelets using SBL-based sparse representations and subsequently implements Wigner-Ville distribution (WVD) on the decomposed wavelets to produce TF spectra. By iteratively solving a Bayesian maximum posterior and a type-II maximum likelihood, SBL-based decomposition can sequentially obtain an optimal number of Ricker wavelets with different peak frequencies or phases from a preset wavelet dictionary, and can simultaneously invert for the associated sparse TF pseudoreflectivity with the prediction uncertainty. The WVD of SBL-based decomposed wavelets can assemble TF distribution of the reconstructed signals to approximately characterize WVD of the original data. Therefore, the linear stack of WVD of all decomposed independent wavelets is immune from both the notorious cross-term interferences of the traditional WVD and random noise. Synthetic data example involving thin beds and laboratorial physical modeling data example involving several known multicave combinations are used to demonstrate the effectiveness of the proposed SBL-based TF analysis method and illustrate its advantages over WVD and the orthogonal matching pursuit-based TF analysis method. The 3-D real seismic data example is adopted to test its application potential for interpreting deep channels and the karst slope fracture zone. The results show that the proposed SBL-based TF method is a potentially effective, stable and high-resolution seismic TF analysis tool even in the presence of thin beds.

关键字：Seismic data interpretation sparse Bayesian learning (SBL) sparse representations time-frequency (TF) analysis

ISSN号：1545-598X

卷、期、页：卷: 16 期: 4 页: 623-627

发表日期：2019-04-01

影响因子：2.761000

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）,ESI（基本科学指标数据库）,EI（工程索引）

发表期刊名称：IEEE Geoscience and Remote Sensing Letters

参与作者：施佩东,高建虎

通讯作者：纪永祯,曾靖

第一作者：袁三一,王尚旭

论文类型：期刊论文

论文概要：袁三一,纪永祯,施佩东,曾靖,高建虎,王尚旭，Sparse Bayesian Learning-Based Seismic High-Resolution Time-Frequency Analysis，IEEE Geoscience and Remote Sensing Letters，2019，卷: 16 期: 4 页: 623-627

论文题目：Sparse Bayesian Learning-Based Seismic High-Resolution Time-Frequency Analysis